Łatuszyński, Krzysztof, Roberts, Gareth O. and Rosenthal, Jeffrey S. (Jeffrey Seth) (2011) Adaptive Gibbs samplers and related MCMC methods. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. (Working papers, Vol.2011).

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Abstract

We consider various versions of adaptive Gibbs and Metropolis- within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the y during a run, by learning as they go in an attempt to optimise the algorithm.We present a cautionary example of how even a simple-seeming adaptive Gibbs sampler may fail to converge.We then present various positive results guaranteeing convergence of adaptive Gibbs samplers under certain conditions.

Item Type:	Working or Discussion Paper (Working Paper)
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Monte Carlo method, Markov processes, Sampling (Statistics)
Series Name:	Working papers
Publisher:	University of Warwick. Centre for Research in Statistical Methodology
Place of Publication:	Coventry
Date:	2011
Volume:	Vol.2011
Number:	No.3
Status:	Not Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Natural Sciences and Engineering Research Council of Canada (NSERC), University of Warwick. Centre for Research in Statistical Methodology, Engineering and Physical Sciences Research Council (EPSRC)
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URI:	http://wrap.warwick.ac.uk/id/eprint/34872

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